Personalizing recommendation diversity based on user personality

Abstract

In recent years, diversity has attracted increasing attention in the field of recommender systems because of its ability of catching users’ various interests by providing a set of dissimilar items. There are few endeavors to personalize the recommendation diversity being tailored to individual users’ diversity needs. However, they mainly depend on users’ behavior history such as ratings to customize diversity, which has two limitations: (1) They neglect taking into account a user’s needs that are inherently caused by some personal factors such as personality; (2) they fail to work well for new users who have little behavior history. In order to address these issues, this paper proposes a generalized, dynamic personality-based greedy re-ranking approach to generating the recommendation list. On one hand, personality is used to estimate each user’s diversity preference. On the other hand, personality is leveraged to alleviate the cold-start problem of collaborative filtering recommendations. The experimental results demonstrate that our approach significantly outperforms related methods (including both non-diversity-oriented and diversity-oriented methods) in terms of metrics measuring recommendation accuracy and personalized diversity degree, especially in the cold-start setting.

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Notes

  1. 1.

    http://whattorent.com.

  2. 2.

    High C means that the user has high score on the personality trait “Conscientiousness”, which is also applied to the other abbreviations (i.e., O for “Openness to Experience”, E for “Extroversion”, A for “Agreeableness”, and N for “Neuroticism”).

  3. 3.

    https://www.douban.com/group/explore.

  4. 4.

    www.whattorent.com/theory.php.

  5. 5.

    The type classification is determined by Douban, and each group belongs to one type.

  6. 6.

    Active user refers to the user who has at least one behavior record during the year 2015 (from Jan. 1 to Dec. 31), e.g., creating a group/topic, joining a group, leaving a comment, recommending or liking a group/topic.

  7. 7.

    To clean the data, we first excluded 118 incomplete answers (5.7%). We then analyzed users’ answers to the personality questionnaire and filtered out all of the contradictory records [e.g., a user rated 5 (out of 5) on two opposite statements “I think I am cold and aloof” and “I think I am considerate and kind to almost everyone”], by which we further removed 252 invalid answers.

  8. 8.

    A POMP score is a linear transformation of any raw metric into a 0–100 scale, where 0 represents the minimum possible score and 100 represents the maximum possible score (Cohen et al. 1999). In this case, \({ Score}_{{ POMP}}=({ Score}_{{ ORI}}-1) \times 25\), where \({ Score}_{{ ORI}}\) ranges from 1 to 5.

  9. 9.

    Similar to other work that often uses genres of movies, cuisine types in restaurants, or topic categories of news stories, to calculate items’ diversity (Eskandanian et al. 2017), we mainly considered the types of groups users have joined.

  10. 10.

    The reason we did not use Cosine similarity measure (Qian et al. 2004) is because it may produce the deviation when two compared vectors are along with the same direction. For example, given three users’ personality vectors (\(ps_a=(1,1,1,1,1)^T\), \(ps_b=(2,2,2,2,2)^T\), and \(ps_c=(5,5,5,5,5)^T\)), we can obtain the Cosine similarity results: \(Sim_{Cosine}(ps_a,ps_b)=Sim_{Cosine}(ps_a,ps_c)=1\), but in fact, user b should be more similar to user a than user c, which can be more accurately identified by the Euclidean distance measure.

  11. 11.

    Users vary in their use of a rating scale (Schafer et al. 2007). For instance, one optimistic user may consistently rate items 4 out of 5 stars, while a pessimistic user may often give 3 starts even though s/he likes the item.

  12. 12.

    https://trec.nist.gov/data/web09.html.

  13. 13.

    \(Improvement percentage (IP)=\frac{Value_{testmodel}-Value_{Baseline}}{Value_{Baseline}}\), where \(Value_{Baseline}\) and \(Value_{testmodel}\) respectively denote the performance of the baseline RB approach and the test model such as PB Greedy.

  14. 14.

    Average improvement percentage (Average IP)=\(\frac{\sum _{n \in N} IP_{Num=n}}{|N|}\), where N refers to the set of training data size (\(N=\{1,3,5,7,10,15,20\}\)).

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Acknowledgements

We thank all participants who took part in our user survey. We also thank reviewers for their suggestions and comments. In addition, we thank Hong Kong Research Grants Council (RGC) for sponsoring the research work (under Project RGC/HKBU12200415).

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Wu, W., Chen, L. & Zhao, Y. Personalizing recommendation diversity based on user personality. User Model User-Adap Inter 28, 237–276 (2018). https://doi.org/10.1007/s11257-018-9205-x

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Keywords

  • Recommender system
  • Diversity
  • Personality traits
  • User survey
  • Greedy re-ranking